Friday, January 29, 2016

Ah ! Another instance of the Great Convergence. This time it is by using the measurement step in compressive sensing as the first layer of a neural network. Intsead of a traditional iterative solver for the reconstruction or the simple one-through least squares step of ELMs, the authors go for a deep architecture much like recently.

Why is this study a big deal ? For the longest time, people in Deep Learning have been using convolutional steps in order to remove the symmetry associated with translation (the Fourier transform allows the comparison of objects translated from one another easily). In this paper, the authors show that, you can break that symmetry early on the very first layers (here it is layer zero) and still do well. This is a big deal in my view.

The goal of this paper is to present a non-iterative and more importantly an
extremely fast algorithm to reconstruct images from compressively sensed (CS)
random measurements. To this end, we propose a novel convolutional neural
network (CNN) architecture which takes in CS measurements of an image as input
and outputs an intermediate reconstruction. We call this network, ReconNet. The
intermediate reconstruction is fed into an off-the-shelf denoiser to obtain the
final reconstructed image. On a standard dataset of images we show significant
improvements in reconstruction results (both in terms of PSNR and time
complexity) over state-of-the-art iterative CS reconstruction algorithms at
various measurement rates. Further, through qualitative experiments on real
data collected using our block single pixel camera (SPC), we show that our
network is highly robust to sensor noise and can recover visually better
quality images than competitive algorithms at extremely low sensing rates of
0.1 and 0.04. To demonstrate that our algorithm can recover semantically
informative images even at a low measurement rate of 0.01, we present a very
robust proof of concept real-time visual tracking application.